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Bowling Green State University

Academic institutionnorthamerica · us
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Research library18linked papers
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Selected work

Representative Papers

Toward Resilient Human-AI Collaboration: A Lifecycle Taxonomy of Sociotechnical Risks and Cascading Failures

Aug 06, 2026

This study addresses the fragmented understanding of sociotechnical risks in human-AI collaboration, which has hindered the identification of common failure mechanisms and effective interventions. To overcome this limitation, the work proposes a unified lifecycle framework encompassing four phases: task allocation, interaction, feedback, and adoption. Through a cross-domain literature review and conceptual modeling, it synthesizes empirical evidence from healthcare, journalism, education, and scientific research to establish the first comprehensive risk taxonomy. The analysis reveals six core risk clusters—including miscalibrated trust, cognitive overload, and responsibility gaps—and elucidates their cascading interdependencies. By moving beyond isolated risk assessments, this research provides a theoretical foundation for resilient human-AI collaboration and informs end-to-end governance strategies and human-centered AI system design.

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Learnable Instance Attention Filtering for Adaptive Detector Distillation

Mar 27, 2026

Existing knowledge distillation methods for object detection typically treat all instances equally and employ heuristic or teacher-only attention filtering mechanisms, overlooking the student model’s learning dynamics and instance-level variations. This work proposes a learnable instance-aware attention filtering framework that, for the first time, integrates the student model’s dynamic learning state into an instance-level attention mechanism. By introducing a trainable instance selector, the method adaptively reweights the importance of each instance, enabling end-to-end adaptive distillation. This approach departs from conventional static or teacher-dominated filtering paradigms and achieves significant performance gains on KITTI and COCO benchmarks: a GFL ResNet-50 student model improves by 2% mAP without additional computational overhead, outperforming current state-of-the-art methods.

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Recent publications

Latest Papers

Toward Resilient Human-AI Collaboration: A Lifecycle Taxonomy of Sociotechnical Risks and Cascading Failures

Aug 06, 2026

This study addresses the fragmented understanding of sociotechnical risks in human-AI collaboration, which has hindered the identification of common failure mechanisms and effective interventions. To overcome this limitation, the work proposes a unified lifecycle framework encompassing four phases: task allocation, interaction, feedback, and adoption. Through a cross-domain literature review and conceptual modeling, it synthesizes empirical evidence from healthcare, journalism, education, and scientific research to establish the first comprehensive risk taxonomy. The analysis reveals six core risk clusters—including miscalibrated trust, cognitive overload, and responsibility gaps—and elucidates their cascading interdependencies. By moving beyond isolated risk assessments, this research provides a theoretical foundation for resilient human-AI collaboration and informs end-to-end governance strategies and human-centered AI system design.

0 citationsRead paper

Learnable Instance Attention Filtering for Adaptive Detector Distillation

Mar 27, 2026

Existing knowledge distillation methods for object detection typically treat all instances equally and employ heuristic or teacher-only attention filtering mechanisms, overlooking the student model’s learning dynamics and instance-level variations. This work proposes a learnable instance-aware attention filtering framework that, for the first time, integrates the student model’s dynamic learning state into an instance-level attention mechanism. By introducing a trainable instance selector, the method adaptively reweights the importance of each instance, enabling end-to-end adaptive distillation. This approach departs from conventional static or teacher-dominated filtering paradigms and achieves significant performance gains on KITTI and COCO benchmarks: a GFL ResNet-50 student model improves by 2% mAP without additional computational overhead, outperforming current state-of-the-art methods.

0 citationsRead paper